The study of prognostics refers to an engineering discipline focused on predicting the future condition of a component and/or a system of components. The science of prognostics is based on the analysis of failure modes, detection of early signs of wear and aging in complex systems and components, and correlation of these signs with an aging profile, or model. Diagnostics is a related discipline involving the diagnosis and isolation of a problem by identifying or determining the nature and circumstances of an existing condition of the complex system.
Potential uses for prognostics and diagnostics include estimation of remaining useful life (RUL) and condition-based maintenance. The discipline that links studies of failure mechanisms to system lifecycle management is often referred to as prognostics and health management (PHM). Technical approaches to prognostics can be categorized broadly into data-driven approaches, model-based approaches, and hybrid approaches.
Model-based prognostics and diagnostics attempt to incorporate physical understanding (physical models) of the system into the estimation of remaining useful life and the isolation of existing faults. Modeling physics can be accomplished at different levels, for example, micro and macro levels. At the micro (material) level, physical models may be embodied by series of dynamic equations that define relationships, at a given time or load cycle, between damage (or degradation) of a system component and environmental conditions under which the system/component are operated.
Macro-level models are the mathematical model at system level, which defines the relationship among system input variables, system state variables, and system measures variables/outputs where the model is often a somewhat simplified representation of the system, for example a lumped parameter model. The trade-off is increased coverage with possibly reducing accuracy of a particular degradation mode. Where this trade-off is permissible, faster prototyping may be the result. However, where systems are complex (e.g., a gas turbine engine), even a macro-level model may be a rather time-consuming and labor-intensive process. As a result, macro-level models not be available in detail for all subsystems.
It is desirable to collect data from various subsystems and subassemblies of a complex system, and then integrate the data into a data management system. In order to support this type of integration, the input format must be able to represent the diagnostic data for any level in the system's hierarchy (e.g., platform, sub-platform, system, subsystem, line-replaceable unit (LRU), circuit assembly, integrated circuit (IC), etc.). Accordingly, a need exists for a mechanism to facilitate such integration. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description of the invention and the appended claims, taken in conjunction with the accompanying drawings and this background of the invention.